Simultaneous Localization and Mapping (SLAM) with 3D Gaussian Splatting (3DGS) enables fast, differentiable rendering and high-fidelity reconstruction across diverse real-world scenes. However, existing 3DGS-SLAM approaches handle measurement reliability implicitly, making pose estimation and global alignment susceptible to drift in low-texture regions, transparent surfaces, or areas with complex reflectance properties. To this end, we introduce VarSplat, an uncertainty-aware 3DGS-SLAM system that explicitly learns per-splat appearance variance. By using the law of total variance with alpha compositing, we then render differentiable per-pixel uncertainty map via efficient, single-pass rasterization. This map guides tracking, submap registration, and loop detection toward focusing on reliable regions and contributes to more stable optimization. Experimental results on Replica (synthetic) and TUM-RGBD, ScanNet, and ScanNet++ (real-world) show that VarSplat improves robustness and achieves competitive or superior tracking, mapping, and novel view synthesis rendering compared to existing studies for dense RGB-D SLAM.

翻译：基于三维高斯泼溅（3DGS）的同步定位与建图（SLAM）技术能够实现快速、可微渲染，并在多样化的真实场景中完成高保真重建。然而，现有的3DGS-SLAM方法仅隐式处理测量可靠性，导致位姿估计与全局配准在低纹理区域、透明表面或具有复杂反射特性的区域易受漂移影响。为此，我们提出VarSplat，一种不确定性感知的3DGS-SLAM系统，其显式学习每个泼溅块的外观方差。通过结合全方差定律与Alpha合成技术，我们进而通过高效的单通道栅格化渲染出可微的逐像素不确定性图。该图引导跟踪、子图配准与闭环检测聚焦于可靠区域，并有助于实现更稳定的优化。在Replica（合成数据集）以及TUM-RGBD、ScanNet和ScanNet++（真实世界数据集）上的实验结果表明，相较于现有稠密RGB-D SLAM研究，VarSplat提升了系统鲁棒性，并在跟踪、建图及新视角合成渲染方面取得了具有竞争力或更优的性能。